Top Engineering Jobs in Hyderabad

Build and improve production LLM agents and AI features through evaluation, experimentation, observability, prompting, context engineering, tool use, and workflow design. Develop backend services, APIs, data models, and feedback pipelines that make agent behavior reliable and measurable. Investigate performance issues, run staged rollouts and production replays, define quality standards, and partner with Product, Data Science, and Sales to deliver business outcomes with appropriate privacy, security, and human-oversight safeguards.

Build and own full-stack product features from ambiguous problems through deployment. Partner with Product and Design, make UX decisions, integrate LLM and agent capabilities into trustworthy product experiences, talk with users, instrument releases, and iterate based on usage. The role requires strong product judgment, user empathy, independent execution, and comfort using AI coding tools while collaborating with AI engineers on model behavior.

Lead technical discovery, design and run POCs, build prototypes and demos using Deepgram SDKs/APIs, advise on architecture (cloud/self-hosted), handle security/compliance across APAC, triage evaluation issues, create reusable assets, and enable sales and post-sales continuity for strategic accounts.

Own architecture and production engineering for regulated HealthTech and FinTech systems handling patient and payment data. Design service boundaries, APIs, data models, security, observability, and scaling strategies; write and review code across the MERN stack; operate cloud infrastructure, Kubernetes, CI/CD, and infrastructure-as-code; make major architectural trade-offs; establish technical standards; manage incidents; and advise clients.

Own production reliability by defining SLIs/SLOs, managing error budgets, building zero-downtime deployment pipelines, and codifying infrastructure with Terraform and Ansible. Monitor systems using observability platforms, lead incident response and root-cause analysis, optimize capacity and costs, automate operations with scripting and AI tools, and participate in shared on-call rotations. The role requires cloud, Kubernetes, CI/CD, Linux, networking, distributed-systems, and high-availability production experience.

Design, build, and operate Databricks-based data and AI platforms using Medallion Architecture, Spark, Delta Lake, and scalable ELT pipelines. Integrate enterprise systems, implement data quality and monitoring, enforce governance and security, manage cloud data lakes, and enable ML/AI workflows with MLflow. Maintain documentation, conduct code reviews, train stakeholders, and report progress against project milestones.

Design, build, test, and deploy smart contracts and decentralized applications. Maintain blockchain integrations and backend services, optimize for security and gas efficiency, contribute to architecture and technical strategy, conduct code reviews, mentor junior engineers, and collaborate with product, frontend, and security teams.

Lead performance marketing, SEO, analytics, lead generation, automation, and AI-powered growth initiatives. Manage paid campaigns, technical and on-page SEO, GA4, tracking, attribution, dashboards, conversion optimization, and marketing ROI. Build automation workflows using n8n, Zapier, Make, APIs, and webhooks, while leveraging AI tools for content, campaign optimization, research, and reporting. Collaborate with Sales, Product, and Engineering teams to drive measurable growth.

Design, develop, and deploy enterprise data pipelines and data products using AWS and Databricks. Build Medallion Architecture pipelines, write Python, PySpark, and SQL code, optimize data workflows, troubleshoot production issues, and implement CI/CD using Databricks Asset Bundles, GitHub Enterprise, and GitHub Actions. Work hands-on across the full data engineering lifecycle in an Agile/Scrum environment.

Coordinate enterprise releases and support deployments across environments. Use Pega Cloud tools, including Deployment Manager, Dev Studio, and Diagnostic Center, to monitor application health during and after releases. Apply SDLC, release management, and environment promotion concepts; provide deployment updates in CAB and release review meetings; coordinate stakeholders; and perform basic troubleshooting and issue triage.

Lead design and development of core SaaS product components on AWS, build and optimize AI-powered features using LLMs, RAG and agentic workflows, collaborate across teams to deliver roadmap value, partner with business teams, and champion engineering best practices and company values.

Design, develop, execute, and maintain automated web test scripts using Selenium or Playwright. Create and run functional, regression, smoke, sanity, and end-to-end tests. Integrate tests into CI/CD, perform API testing, log and track defects, and collaborate with developers and product teams in Agile ceremonies to ensure software quality.

Manage healthcare provider credentialing records, documents, communications, applications, approvals, and data accuracy. Coordinate with providers, credentialing companies, internal specialists, and credentialing committees. Track and report credentialing status, reconcile information gaps, maintain electronic and physical files, prepare approval letters and presentations, record meeting minutes, and support special projects. The role requires healthcare credentialing experience, data and spreadsheet skills, strong communication, attention to detail, and flexibility for after-hours or weekend work.

Serve as a technical product-facing data engineer translating product requirements into build-ready engineering artifacts. Partner with product, architecture, and platform teams to design and validate data models, SQL queries, pipelines, APIs, and data quality checks. Contribute hands-on to schema definitions, metadata, pipeline validation, automation, and observability to enable reliable cloud data platform delivery and accelerate engineering execution.

Leads hands-on engineering for complex enterprise Microsoft Power Platform implementations. Designs Dataverse architectures, builds Power Apps and Power Automate solutions, develops custom C#/.NET and JavaScript/TypeScript extensions, and integrates Azure and Microsoft enterprise systems. Establishes ALM and CI/CD practices, leads technical discovery and architecture discussions, resolves complex engineering issues, mentors engineers, and advises clients on appropriate Power Platform versus custom technology solutions.

Early-career technologist supporting design, development, testing, deployment, and maintenance of applications, data pipelines, automations, and service management solutions. Assist requirements gathering, troubleshooting, QA, data tasks, and user support while following standards and learning new tools under senior guidance.

Develop and support Microsoft Dynamics 365 Finance & Operations solutions, troubleshoot technical, performance, functional, and integration issues, and deliver enhancements, bug fixes, integrations, and deployments. The role includes incident ownership, documentation, monitoring, automation, technical design, peer reviews, stakeholder communication, and mentoring junior engineers. Candidates collaborate with global teams and contribute to Power Platform and Azure integration initiatives.

Designs, implements, and optimizes AI-powered Amazon Connect contact center solutions. Develops voice and chat experiences using Amazon Lex and generative AI, integrates CRM, IVR, telephony, and enterprise applications, and builds intelligent routing, chatbots, agent assistance, and self-service capabilities using Amazon Bedrock, Amazon Q, Lambda, APIs, and AWS services.

Designs and executes manual and automated tests across functional, regression, integration, system, and user acceptance testing. Documents and tracks defects, maintains test plans and reports, validates APIs and databases, and collaborates with development and business teams. Participates in Agile ceremonies, reviews requirements, supports production issue analysis, and verifies software quality before deployment.

Design, develop, test, and maintain Java/J2EE backend and frontend features. Build UI components with HTML, JavaScript, and CSS; write and optimize SQL queries; perform code reviews and unit testing; troubleshoot production issues; collaborate with QA, product, and engineering teams; and contribute to technical design and documentation.

Own production reliability across cloud environments by managing infrastructure, building zero-downtime CI/CD pipelines, codifying infrastructure with Terraform and Ansible, and driving observability. Define SLIs, SLOs, and error budgets; lead incident response, root-cause analysis, and post-incident reviews; optimize cloud costs and capacity; automate AI-powered operational workflows; and participate in on-call rotations.

Develop agentic AI and generative AI solutions using Python, FastAPI, LLMs, RAG, AI agents, LangChain, and LangGraph. Build and deploy cloud-based applications on GCP, integrate vector databases, and implement CI/CD pipelines. The role requires strong software engineering experience and hands-on experience with agentic AI and GenAI technologies.

Design, develop, and modernize enterprise-scale Azure data platforms. Lead solution architecture, refactor and optimize PySpark/Databricks pipelines, implement ETL/ELT and lakehouse/warehousing solutions, ensure performance, data quality, governance, and mentor junior engineers.

Expand end-to-end, functional, UI, integration, regression, black box, and system test coverage across subscription and booking systems. Maintain Quicksub and Trace-based test infrastructure, perform manual validation of feature changes, identify regressions, and document test cases, results, and defects for engineering follow-up.

Develop and maintain high-performance backend services in Rust using asynchronous programming, REST APIs, databases, Redis, messaging systems, Docker, Kubernetes, and AWS. Responsibilities include writing tested and maintainable code, participating in code reviews, troubleshooting production issues, optimizing performance, implementing monitoring and error handling, and collaborating with senior developers, QA, DevOps, and product teams.